A General Framework for Predictive Business Process Monitoring

As organizations gain awareness of the potential business value locked in their process execution event logs, “evidence-based” business process management (BPM) becomes a common tool for process analysts. In contrast to traditional process monitoring techniques which are typically performed using data from running process instances only, predictive evidence-based BPM methods tap also into historical data, to allow process workers to respond, in real-time, to specific process performance issues and compliance violations as they arise or even before they arise. In previous work, various approaches have been proposed to address typical predictive process monitoring problems, such as whether a running process instance will meet its performance targets, or when will an instance be finally finished. However, these approaches are rather ad-hoc and lack generality, as they tackle only particular, pre-defined aspects of predictive monitoring and often only work with specific characteristics of the dataset. The proposed research project aims at developing a general and robust framework for predictive process monitoring that will address a variety of process monitoring tasks such as predicting the outcome of individual activities or of the whole process instance, or predicting the completion path of an instance.